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      "cell_type": "markdown",
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      "source": [
        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/texture.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Image/texture.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Image/texture.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('Installing geemap ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import ee\n",
        "import geemap"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "import math\n",
        "\n",
        "# Load a high-resolution NAIP image.\n",
        "image = ee.Image('USDA/NAIP/DOQQ/m_3712213_sw_10_1_20140613')\n",
        "\n",
        "# Zoom to San Francisco, display.\n",
        "Map.setCenter(-122.466123, 37.769833, 17)\n",
        "Map.addLayer(image, {'max': 255}, 'image')\n",
        "\n",
        "# Get the NIR band.\n",
        "nir = image.select('N')\n",
        "\n",
        "# Define a neighborhood with a kernel.\n",
        "square = ee.Kernel.square(**{'radius': 4})\n",
        "\n",
        "# Compute entropy and display.\n",
        "entropy = nir.entropy(square)\n",
        "Map.addLayer(entropy,\n",
        "             {'min': 1, 'max': 5, 'palette': ['0000CC', 'CC0000']},\n",
        "             'entropy')\n",
        "\n",
        "# Compute the gray-level co-occurrence matrix (GLCM), get contrast.\n",
        "glcm = nir.glcmTexture(**{'size': 4})\n",
        "contrast = glcm.select('N_contrast')\n",
        "Map.addLayer(contrast,\n",
        "             {'min': 0, 'max': 1500, 'palette': ['0000CC', 'CC0000']},\n",
        "             'contrast')\n",
        "\n",
        "# Create a list of weights for a 9x9 kernel.\n",
        "list = [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
        "# The center of the kernel is zero.\n",
        "centerList = [1, 1, 1, 1, 0, 1, 1, 1, 1]\n",
        "# Assemble a list of lists: the 9x9 kernel weights as a 2-D matrix.\n",
        "lists = [list, list, list, list, centerList, list, list, list, list]\n",
        "# Create the kernel from the weights.\n",
        "# Non-zero weights represent the spatial neighborhood.\n",
        "kernel = ee.Kernel.fixed(9, 9, lists, -4, -4, False)\n",
        "\n",
        "# Convert the neighborhood into multiple bands.\n",
        "neighs = nir.neighborhoodToBands(kernel)\n",
        "\n",
        "# Compute local Geary's C, a measure of spatial association.\n",
        "gearys = nir.subtract(neighs).pow(2).reduce(ee.Reducer.sum()) \\\n",
        "             .divide(math.pow(9, 2))\n",
        "Map.addLayer(gearys,\n",
        "             {'min': 20, 'max': 2500, 'palette': ['0000CC', 'CC0000']},\n",
        "             \"Geary's C\")\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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